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研究生(外文):Shuo-Ting Tsai
論文名稱(外文):Lung nodule detection using ensemble classifier in CT images
指導教授(外文):Chien-Cheng Lee
口試委員(外文):Hsu-Yung ChengChun-Rong Huang
外文關鍵詞:lung nodulecomputed tomographyensemble classifier
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肺癌是癌症死因之首,主要原因是因為多數肺癌患者被檢測出肺癌時,已是第三期或末期階段;肺結節是肺部微小的異常組織,有可能是惡性組織即肺癌早期病灶;因此,肺結節偵測能夠達成早期發現早期治療的目標。本研究提出一套應用整體式分類技術於CT影像之肺結節偵測系統來提供放射科醫師評估的第二意見;本系統包含肺部區域分割、候選肺結節偵測與候選肺結節分類等步驟;在肺部區域分割中,本研究應用自適應二值化演算法,由原始斷層掃描分割肺部區域,並藉由分析邊界結構來修正肺部邊界;在候選肺結節步驟中,本研究先利用自適應二值化演算法找出可疑區域,隨後藉由分析連續影像的連通元件來初步偵測候選肺結節,再藉由初步偵測結果的分析來結合成更完整的候選肺結節;最後,在候選肺結節分類中,本研究透過自組織映射網路挑選訓練樣本並結合整體式分類技術來進行分類,最後達到100% Sensitivity與86.0717% Specificity。
Lung cancer death rate is the highest because most people diagnosed cancer in the third or last stage. Lung nodule is the abnormal growth tissue, and it may become a malignant tumor. Therefore, lung nodule detection is one of the method for early cancer discovery. This paper presents a method for lung nodule detection in CT images based on the ensemble classifier. This lung nodule computer aided detection system includes lung parenchyma segmentation, nodule candidate detection, and nodule candidate classification. First, the system uses the image intensity to apply an adaptive thresholding algorithm for the lung parenchyma segmentation, and the lung region boundary will be fixed by the contour analysis method. Second, the system finds the region of interest using an adaptive thresholding algorithm, and lung nodule are roughly detected by the connected component analysis. In order to obtain the complete 3D structure, the system merges the rough detection results if they conformed the merging condition. Finally, the system applies a self-organizing map algorithm to select the negative samples for the training data, and applies an ensemble classifier to recognize the nodule candidates. The experiment result shows the sensitivity is 100% and the specificity is 86.017%.
書名頁 II
審定書 III
中文摘要 IV
英文摘要 V
誌謝 VII
第一章、 序論 1
1.1 研究背景 1
1.2 研究目標與貢獻 3
1.3 論文架構 3
第二章、 文獻探討 4
2.1 肺部區域分割相關研究 4
2.2 候選肺結節偵測相關研究 7
2.3 候選肺結節分類相關研究 10
第三章、 研究方法 15
3.1 肺結節偵測流程 15
3.2 肺部區域影像分割 16
3.2.1斷層掃描介紹與肺部區域分割 17
3.2.2肺部區域邊界修正 22
3.3 候選肺結節偵測 24
3.3.1候選肺結節初步偵測 24
3.3.2候選肺結節結合 26
3.4 候選肺結節分類 27
3.4.1特徵擷取 28
3.4.2Negative訓練樣本挑選 30
3.4.3整體式分類器 34 多層感知器 35 支援向量機 38 40 整體式分類器 40
第四章、 實驗結果 43
4.1 資料來源 43
4.2 肺結節偵測效能評估方式 45
4.3 實驗流程 46
4.3.1肺部區域分割與候選肺結節偵測結果 47
4.3.2候選肺結節標記與分類 50
4.4 肺結節偵測結果與分類結果 51
第五章、 結論與未來展望 57
參考文獻 58

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